In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images
(restoration of rich details in hazy image). We focus on the proposed solutions and their …

Image restoration involves recovering a high-quality clean image from its degraded version.
Deep learning-based methods have significantly improved image restoration performance …

In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …

In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image
pairs, many recent methods attempted to improve models' generalization ability by training …

Single image dehazing is a challenging ill-posed problem due to the severe information
degeneration. However, existing deep learning based dehazing methods only adopt clear …

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …

This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE),
which formulates light enhancement as a task of image-specific curve estimation with a deep …